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---
task_categories:
- text-classification
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset: Core Intents
The **Core Intents** dataset contains feedback-related utterances classified into seven key categories. This dataset is designed to help train voice assistants in handling fundamental types of feedback and adjusting their behavior accordingly.
## Labels
The dataset includes the following labels:
- **ask_clarify**: Requests for clarification, where the user seeks clearer explanations.
- **ask_confirmation**: Requests for confirmation, where the user asks to validate a previous command or statement.
- **ask_repeat**: Requests for repetition, where the user asks for something to be repeated.
- **negative_feedback**: Negative responses or corrections, indicating dissatisfaction or disagreement.
- **positive_feedback**: Positive responses, expressing satisfaction or agreement.
- **neutral_feedback**: Neutral responses, where the user shows indifference or general acceptance.
- **stop**: Commands indicating the user wants to halt an action or process.
## Examples
Sample utterances for each label:
- **ask_clarify**:
- "your response was not clear"
- "your words were not so clear to me"
- **ask_confirmation**:
- "can you check and confirm my last command please"
- "can you check and confirm it"
- **ask_repeat**:
- "would you try again please"
- "would you try what you said again"
- **negative_feedback**:
- "command wrong"
- "dammit, I did not say it"
- **positive_feedback**:
- "thanks, that's amazing"
- "that is excellent, much appreciated"
- **neutral_feedback**:
- "anything is fine"
- "any one would be good to me"
## Purpose
This dataset aims to cover core interactions a voice assistant should manage to provide responsive and adaptive behavior in real-time communication. It can be used in tasks such as:
- Intent classification
- Dialogue system training
- Feedback management in voice assistants
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